Maddox, W. T. (2002).
Toward a unified theory of decision criterion learning in perceptual categorization.
Journal of the Experimental Analysis of Behavior,
78, 567-607.
Optimal decision criterion placement maximizes expected reward
and requires sensitivity to the category base rates (prior
probabilities) and payoffs (costs and benefits of incorrect and
correct responding). When base rates are unequal, human decision
criterion is nearly optimal, but when payoffs are unequal,
suboptimal decision criterion placement is observed, even when
the optimal decision criterion is identical in both cases. A
series of studies are reviewed that examine the generality of
this finding, and a unified theory of decision criterion learning
is described (Maddox & Dodd, 2001). The theory assumes that
two critical mechanisms operate in decision criterion learning.
One mechanism involves competition between reward and accuracy
maximization: The observer attempts to maximize reward, as
instructed, but also places some importance on accuracy
maximization. The second mechanism involves a flat-maxima
hypothesis that assumes that the observer's estimate of the
reward-maximizing decision criterion is determined from the
steepness of the objective reward function that relates expected
reward to decision criterion placement. Experiments used to
develop and test the theory require each observer to complete a
large number of trials and to participate in all conditions of
the experiment. This provides maximal control over the
reinforcement history of the observer and allows a focus on
individual behavioral profiles. The theory is applied to decision
criterion learning problems that examine category
discriminability, payoff matrix multiplication and addition
effects, the optimal classifier's independence assumption, and
different types of trial-by-trial feedback. In every case the
theory provides a good account of the data, and, most important,
provides useful insights into the psychological processes
involved in decision criterion learning.
Key words: decision criterion learning, category learning, base rates,
cost-benefits, optimality, multivariate signal-detection theory